To obtain an effective data mining method for cable-stayed bridge damage diagnosis, the algorithm of the cable-stayed bridge damage diagnosis model based on data mining was studied, and a data mining method is proposed. This method is oriented to the damage diagnosis of cable-stayed bridges. After algorithm comparison, the support vector machine (SVM) and limit gradient-boosting (XGBoost) algorithms, with advantages in damage location and quantification, are combined and optimized to obtain the damage diagnosis model for cable-stayed bridges. First, a refined benchmark finite element model is established by Abaqus, and postprocessing data such as vibration frequency and modal curvature are used as a data mining dataset. Second, feature se-lection is conducted, and the damage-sensitive modal curvature change rate index is selected as the feature of data mining. Next, the SVM and XGBoost algorithms are optimized by grid and random search, and the optimized SVM and XGBoost algorithms are used to locate and quantify the damage. Finally, the damage diagnosis model for cable-stayed bridges is obtained. Taking a cable-stayed bridge as an example, the proposed method is applied and analyzed, and the results show the effectiveness of the proposed method.

To solve the problem that finite element analysis and calculation need to be repeated during model updates, which leads to tedious and inefficient correction work, ABAQUS (a powerful finite element software for engineering simulation) is used to develop a user-friendly graphical user interface platform for automatic completion of this update for cable-stayed bridges. In this paper, a method for establishing the benchmark finite element model of cable-stayed bridges based on ABAQUS and influence matrix (IM)-hunter prey optimization (HPO) is proposed. The method is based on the secondary development of ABAQUS to realize the modules of sensitivity analysis, influence matrix, and optimization. According to the sensitivity analysis module, the influence of the change in the finite element model parameters on the static response of a cable-stayed bridge is studied, and the cable area is used as the parameter to be corrected. The influence matrix module is used to obtain the IM of the cable area to girder displacement and cable force under a static load. The objective function is constructed by combining the measured displacement and cable force data using the optimization module, and the global optimal solution of the parameters to be corrected in the objective function is found by HPO. Finally, the solution is sent to the finite element model for correction, and the corrected displacement and cable force data are compared with the measured data. The results show that the modified calculation results obtained by this method are in good agreement with the measured results. The modified finite element model can be used as presented.

To solve the problem that the damage diagnosis model of cable-stayed bridges cannot quantitatively evaluate its validity, the verification method of the diagnosis model is investigated. A model validation method [Root-Mean-Square Error (RMSE)-Statistical Hypothesis Test (SHT), R–S] that combines the RMSE evaluation index and the SHT is proposed and applied to the reliability evaluation of the cable-stayed bridge data mining damage diagnosis model. First, the R–S method evaluates the diagnostic effect of the model via the evaluation index. Second, the SHT method is used to quantitatively compare the consistency between the output data of the diagnostic model and the actual structure output data. Finally, the validity of the model is quantitatively evaluated. The application process of the proposed method is demonstrated by an example. The results show that the diagnostic model has strong reliability and that the confidence level Pδ exceeds 90%, which indicates the effectiveness of the diagnostic model verification method.